Identifying the Effect of Cognitive Motivation with the Method Based on Temporal Association Rule Mining Concept

Tustanah Phukhachee, Suthathip Maneewongvatana, Chayapol Chaiyanan, Keiji Iramina, Boonserm Kaewkamnerdpong

研究成果: ジャーナルへの寄稿学術誌査読

1 被引用数 (Scopus)

抄録

Being motivated has positive influences on task performance. However, motivation could result from various motives that affect different parts of the brain. Analyzing the motivation effect from all affected areas requires a high number of EEG electrodes, resulting in high cost, inflexibility, and burden to users. In various real-world applications, only the motivation effect is required for performance evaluation regardless of the motive. Analyzing the relationships between the motivation-affected brain areas associated with the task’s performance could limit the required electrodes. This study introduced a method to identify the cognitive motivation effect with a reduced number of EEG electrodes. The temporal association rule mining (TARM) concept was used to analyze the relationships between attention and memorization brain areas under the effect of motivation from the cognitive motivation task. For accuracy improvement, the artificial bee colony (ABC) algorithm was applied with the central limit theorem (CLT) concept to optimize the TARM parameters. From the results, our method can identify the motivation effect with only FCz and P3 electrodes, with 74.5% classification accuracy on average with individual tests.

本文言語英語
論文番号2857
ジャーナルSensors
24
9
DOI
出版ステータス出版済み - 5月 2024

!!!All Science Journal Classification (ASJC) codes

  • 分析化学
  • 情報システム
  • 原子分子物理学および光学
  • 生化学
  • 器械工学
  • 電子工学および電気工学

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